eHealth Literacy Instruments: Systematic Review of Measurement Properties

Background: The internet is now a major source of health information. With the growth of internet users, eHealth literacy has emerged as a new concept for digital health care. Therefore, health professionals need to consider the eHealth literacy of consumers when providing care utilizing digital health technologies. Objective: This study aimed to identify currently available eHealth literacy instruments and evaluate their measurement properties to provide robust evidence to researchers and clinicians who are selecting an eHealth literacy instrument. Methods: We conducted a systematic review and meta-analysis of self-reported eHealth literacy instruments by applying the updated COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) methodology. Results: This study included 7 instruments from 41 articles describing 57 psychometric studies, as identified in 4 databases (PubMed, CINAHL, Embase, and PsycInfo). No eHealth literacy instrument provided evidence for all measurement properties. The eHealth literacy scale (eHEALS) was originally developed with a single-factor structure under the definition of eHealth literacy before the rise of social media and the mobile web. That instrument was evaluated in 18 different languages and 26 countries, involving diverse populations. However, various other factor structures were exhibited: 7 types of two-factor structures, 3 types of three-factor structures, and 1 bifactor structure. The transactional eHealth literacy instrument (TeHLI) was developed to reflect the broader concept of eHealth literacy and was demonstrated to have a sufficient low-quality and very low-quality evidence for content validity (relevance, comprehensiveness, and comprehensibility) and sufficient high-quality evidence for structural validity and internal consistency; however, that instrument has rarely been evaluated. Conclusions: The eHealth literacy scale was the most frequently investigated instrument. However, it is strongly recommended that the instrument's content be updated to reflect recent advancements in digital health technologies. In addition, the transactional eHealth literacy instrument needs improvements in content validity and further psychometric studies to increase the credibility of its synthesized evidence.ope

Electronic health literacy in Swiss-German parents : cross-sectional study of eHealth literacy scale unidimensionality

Parents often use digital media to search for information related to their children's health. As the quantity and quality of digital sources meant specifically for parents expand, parents' digital health literacy is increasingly important to process the information they retrieve. One of the earliest developed and widely used instruments to assess digital health literacy is the self-reported eHealth Literacy Scale (eHEALS). However, the eHEALS has not been psychometrically validated in a sample of parents. Given the inconsistency of the eHEALS underlying factor structure across previous reports, it is particularly important for validation to occur

Proposing a Transactional Model of eHealth Literacy: Concept Analysis

Background: Electronic health (eHealth) literacy was conceptualized in 2006 as the ability of internet users to locate, evaluate, and act upon web-based health information. Now, advances in eHealth technology have cultivated transactional opportunities for patients to access, share, and monitor health information. However, empirical evidence shows that existing models and measures of eHealth literacy have limited theoretical underpinnings that reflect the transactional capabilities of eHealth. This paper describes a conceptual model based on the Transactional Model of Communication (TMC), in which eHealth literacy is described as an intrapersonal skillset hypothesized as being dynamic; reciprocal; and shaped by social, relational, and cultural contexts. Objective: The objective of our study was to systematically examine eHealth literacy definitions, models, and measures to propose a refined conceptual and operational definition based on the TMC. Methods: Walker and Avant's concept analysis method was used to guide the systematic review of eHealth literacy definitions (n=10), rating scales (n=6), models (n=4), and peer-reviewed model applications (n=16). Subsequent cluster analyses showed salient themes across definitions. Dimensions, antecedents, and consequences reflected in models and measures were extracted and deductively analyzed based on codes consistent with the TMC. Results: Systematic review evidence revealed incongruity between operational eHealth literacy included in definitions compared with literacies included within models and measures. Theoretical underpinnings of eHealth literacy also remain dismal. Despite the transactional capabilities of eHealth, the role of "communication" in eHealth literacy remains underdeveloped and does not account for physical and cognitive processing abilities necessary for multiway transactions. Conclusions: The Transactional Model of eHealth Literacy and a corresponding definition are proposed. In this novel model, eHealth literacy comprises a hierarchical intrapersonal skillset that mediates the reciprocal effect of contextual factors (ie, user oriented and task oriented) on patient engagement in health care. More specifically, the intrapersonal skillset counteracts the negative effect of "noise" (or impediments) produced by social and relational contexts. Cutting across health and technology literacies, the intrapersonal skillset of eHealth literacy is operationalized through four literacies that correspond with discrete operative skills: (1) functional (ie, locate and understand); (2) communicative (ie, exchange); (3) critical (ie, evaluate); and (4) translational (ie, apply)

Exploring the Measurement Properties of the eHealth Literacy Scale (eHEALS) Among Baby Boomers: A Multinational Test of Measurement Invariance

BACKGROUND: The eHealth Literacy Scale (eHEALS) is one of only a few available measurement scales to assess eHealth literacy. Perhaps due to the relative paucity of such measures and the rising importance of eHealth literacy, the eHEALS is increasingly a choice for inclusion in a range of studies across different groups, cultures, and nations. However, despite its growing popularity, questions have been raised over its theoretical foundations, and the factorial validity and multigroup measurement properties of the scale are yet to be investigated fully. OBJECTIVE: The objective of our study was to examine the factorial validity and measurement invariance of the eHEALS among baby boomers (born between 1946 and 1964) in the United States, United Kingdom, and New Zealand who had used the Internet to search for health information in the last 6 months. METHODS: Online questionnaires collected data from a random sample of baby boomers from the 3 countries of interest. The theoretical underpinning to eHEALS comprises social cognitive theory and self-efficacy theory. Close scrutiny of eHEALS with analysis of these theories suggests a 3-factor structure to be worth investigating, which has never before been explored. Structural equation modeling tested a 3-factor structure based on the theoretical underpinning to eHEALS and investigated multinational measurement invariance of the eHEALS. RESULTS: We collected responses (N=996) to the questionnaires using random samples from the 3 countries. Results suggest that the eHEALS comprises a 3-factor structure with a measurement model that falls within all relevant fit indices (root mean square error of approximation, RMSEA=.041, comparative fit index, CFI=.986). Additionally, the scale demonstrates metric invariance (RMSEA=.040, CFI=.984, ΔCFI=.002) and even scalar invariance (RMSEA=.042, CFI=.978, ΔCFI=.008). CONCLUSIONS: To our knowledge, this is the first study to demonstrate multigroup factorial equivalence of the eHEALS, and did so based on data from 3 diverse nations and random samples drawn from an increasingly important cohort. The results give increased confidence to researchers using the scale in a range of eHealth assessment applications from primary care to health promotions

Measures of eHealth literacy: options for the Malaysian population

Health literacy is an important component to the self-management of one’s health decisions. As information on the Internet becomes more easily accessible, individuals need to be properly equipped to seek and evaluate the health information available to them. Digital health literacy, or eHealth literacy, refers to an array of skills required to properly seek, access, understand and apply health information on the Internet. To date, a number of different models and instruments have been developed to measure eHealth literacy, including the eHealth Literacy Scale (eHEALS), the eHealth Literacy Framework (eHLF), the Digital Health Literacy Instrument (DHLI), the eHealth Literacy Questionnaire (eHLQ), the eHealth Literacy Assessment Toolkit (eHLA) and the Transactional eHealth Literacy Instrument (TeHLI). While some of these instruments rely on the individual’s self-assessment of eHealth literacy, others also test for functional information, communication and technological competencies. This paper presents these different eHealth literacy instruments and how they have been applied in current research. The similarities and differences between the instruments are examined. The discussion concludes with a summary of the measures and potential ways forward in developing an eHealth Literacy instrument for the Malaysian population. It is hoped that this paper may benefit researchers, health care providers, practitioners, ministries and governing bodies interested in understanding eHealth literacy and its measures

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Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

A Patient-Centered Framework for Evaluating Digital Maturity of Health Services: A Systematic Review

© Kelsey Flott, Ryan Callahan, Ara Darzi, Erik Mayer.Background: Digital maturity is the extent to which digital technologies are used as enablers to deliver a high-quality health service. Extensive literature exists about how to assess the components of digital maturity, but it has not been used to design a comprehensive framework for evaluation. Consequently, the measurement systems that do exist are limited to evaluating digital programs within one service or care setting, meaning that digital maturity evaluation is not accounting for the needs of patients across their care pathways. Objective: The objective of our study was to identify the best methods and metrics for evaluating digital maturity and to create a novel, evidence-based tool for evaluating digital maturity across patient care pathways. Methods: We systematically reviewed the literature to find the best methods and metrics for evaluating digital maturity. We searched the PubMed database for all papers relevant to digital maturity evaluation. Papers were selected if they provided insight into how to appraise digital systems within the health service and if they indicated the factors that constitute or facilitate digital maturity. Papers were analyzed to identify methodology for evaluating digital maturity and indicators of digitally mature systems. We then used the resulting information about methodology to design an evaluation framework. Following that, the indicators of digital maturity were extracted and grouped into increasing levels of maturity and operationalized as metrics within the evaluation framework. Results: We identified 28 papers as relevant to evaluating digital maturity, from which we derived 5 themes. The first theme concerned general evaluation methodology for constructing the framework (7 papers). The following 4 themes were the increasing levels of digital maturity: resources and ability (6 papers), usage (7 papers), interoperability (3 papers), and impact (5 papers). The framework includes metrics for each of these levels at each stage of the typical patient care pathway. Conclusions: The framework uses a patient-centric model that departs from traditional service-specific measurements and allows for novel insights into how digital programs benefit patients across the health system

Electronic Health Literacy Across the Lifespan: Measurement Invariance Study

Background: Electronic health (eHealth) information is ingrained in the healthcare experience to engage patients across the lifespan. Both eHealth accessibility and optimization are influenced by lifespan development, as older adults experience greater challenges accessing and using eHealth tools as compared to their younger counterparts. The eHealth Literacy Scale (eHEALS) is the most popular measure used to assess patient confidence locating, understanding, evaluating, and acting upon online health information. Currently, however, the factor structure of the eHEALS across discrete age groups is not well understood, which limits its usefulness as a measure of eHealth literacy across the lifespan. Objective: The purpose of this study was to examine the structure of eHEALS scores and the degree of measurement invariance among US adults representing the following generations: Millennials (18-35-year-olds), Generation X (36-51-year-olds), Baby Boomers (52-70-year-olds), and the Silent Generation (71-84-year-olds). Methods: Millennials (N=281, mean 26.64 years, SD 5.14), Generation X (N=164, mean 42.97 years, SD 5.01), and Baby Boomers/Silent Generation (N=384, mean 62.80 years, SD 6.66) members completed the eHEALS. The 3-factor (root mean square error of approximation, RMSEA=.06, comparative fit index, CFI=.99, Tucker-Lewis index, TLI=.98) and 4-factor (RMSEA=.06, CFI=.99, TLI=.98) models showed the best global fit, as compared to the 1- and 2-factor models. However, the 4-factor model did not have statistically significant factor loadings on the 4th factor, which led to the acceptance of the 3-factor eHEALS model. The 3-factor model included eHealth Information Awareness, Search, and Engagement. Pattern invariance for this 3-factor structure was supported with acceptable model fit (RMSEA=.07, Δχ2=P>.05, ΔCFI=0). Compared to Millennials and members of Generation X, those in the Baby Boomer and Silent Generations reported less confidence in their awareness of eHealth resources (P<.001), information seeking skills (P=.003), and ability to evaluate and act on health information found on the Internet (P<.001). Results: Young (18-48-year olds, N=411) and old (49-84-year olds, N=419) adults completed the survey. A 3-factor model had the best fit (RMSEA=.06, CFI=.99, TLI=.98), as compared to the 1-factor, 2-factor, and 4-factor models. These 3-factors included eHealth Information Awareness (2 items), Information Seeking (2 items), and Information and Evaluation (4 items). Pattern invariance was supported with the acceptable model fit (RMSEA=.06, Δχ2=P>.05, ΔCFI=0). Compared with younger adults, older adults had less confidence in eHealth resource awareness (P<.001), information seeking skills (P<.01), and ability to evaluate and act upon online health information (P<.001). Conclusions: The eHEALS can be used to assess, monitor uniquely, and evaluate Internet users’ awareness of eHealth resources, information seeking skills, and engagement abilities. Configural and pattern invariance was observed across all generation groups in the 3-factor eHEALS model. To meet gold the standards for factor interpretation (ie, 3 items or indicators per factor), future research is needed to create and assess additional eHEALS items. Future research is also necessary to identify and test items for a fourth factor, one that captures the social nature of eHealth